A qualitative and quantitative preliminary analysis of a task design web-based tool and its applications for teachereducation
Bibliographic record
Abstract
Abstract Task-based language teaching (TBLT) has been consolidated as a research-based teaching perspective over the last four decades. At its core, task and materials design have been identified as key to successful task-based implementation (Bryfonski, forthcoming). Yet teachers are often left to their own resources when it comes to actual design. The taskGen project has brought together knowledge from second language acquisition, Natural Language Processing (NLP), interaction design, design thinking, and computer engineering to provide a solution to the problem. The goal of this article is threefold: firstly, a brief historical overview contextualizes the issue of task design in TBLT from complementary cognitive and teacher education perspectives; secondly, a web-based tool is presented that assists and trains teachers to design, organize, automate and share tasks. TaskGen assists the design of task structure through pre-tasks, tasks, and post-tasks, while focus on form is achieved through NLP tools. Tasks can be cloned to create simple or more complex versions, and they can also be shared and cloned by other teachers/designers. Thirdly, results from a qualitative study and a quantitative one are analyzed and presented. The former taps into teachers’ perceptions of task design by showing the kind of mental processes involved in decision-making during task design. The latter draws on big data on tool use by teachers, and it measures the impact of training with the tool on teachers’ choices during task design. Overall results of the two studies illustrate the cyclical nature of task design, the central role of focus on form, and the need for task design to be integrated in teacher education in order to achieve its full potential.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.031 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".